Creating species lists
Each country team first will make a list of at least 100 candidate species for which there is likely some data for indicator 1 and/or 2.‘Likely to have some data’ means that the species are notrecently discovered/ poorly known/ very hard to document population size (e.g. they are countable by observation, camera trap, etc.), do not have taxonomic disputes, etc. This is the list of species to try to collect data for.
Following are two ways to make this list of 100 species, though other approaches or a blend of approaches is fine.
First, compose a list of species at the country level that a national biodiversity expert or panel of experts thinks might have data. Then, ‘cross check’ this list against relevant sources of data to narrow it down (e.g. removing species for which there are no published reports, articles, websites, databases, or experts available). This approach could lead to over-representation of well-known, flagship, or economically important species.
Choose one or two prominent data sources (e.g. recovery plans or similar), list all species in that data source, and pick species from this list in a stratified random fashion to cover taxonomy, habitat, etc.. For example, this might involve going through recovery plans for all federally listed Endangered Species, the national Red List, or other lists of conservation concern (e.g. Annex II, IV and V species of the EU Habitats Directive- a defined list of policy importance). This could lead to overrepresentation of species of conservation concern/ underrepresentation of common or ”least concern” species. Many countries have Red Lists for various taxonomic groups. These lists would be one way to select tens to hundreds of species per country across taxonomic groups and ensure each national RL status is represented (Endangered, Least Concern, etc). Note: many LC IUCN species are nevertheless of local or regional conservation concern, and are declining rapidly, etc. so should not be ignored.
It is vital to document how the list is developed in order to identify any biases (e.g. mostly common species). In this project, and in the first use of the indicators by a country for National Reporting,it is acceptable to have some biases , but as data quality and collection efforts improve, biases should decrease. Surveying multiple data sources may be needed (for example: scope the Red List to see what species have data available, then consult with experts on other data sources).
It is not necessary for all chosen species to have high quality data across their range. While indicators would be more accurate if all species have data for all populations, complete population data may only be available rarely. It is ok if data are available for only one of the two indicators or for only some populations of a species (as explained below). Moreover, upon investigation, species initially deemed likely to have some data, may actually have insufficient data to calculate either indicator. Species should not be removed from the list after the initial list is made. We will calculate the indicators with and without various types and levels of missing data.
There are some species where it will be particularly hard or impossible to quantify Indicators 1 and 2, and they should be excluded from the species list. For example, evaluation of the Ne>500 criterion will be hard to implement in species where natural subpopulations are typically very large and/or hard to measure, such as microcrustaceans, many insects, some fungi, highly clonal organisms, some plants with deep soil seed banks (where all ‘individuals’ cannot be counted). Populations of such species can also grow in a short amount of time to very large numbers and have large levels of standing genetic variation (Chaturvedi et al. 2021). We advise not attempting to include such species in a country’s first evaluation of these indicators due to difficulty in finding and interpreting data.